Post-Doctoral Research Visit F/M
il y a 1 mois
Contexte et atouts du poste
The MiMove team at Inria Paris (
Mission confiée
The proposed postdoc research will more particularly focus on the topic of IoT systems in the intelligent edge-fog-cloud compute continuum. Designing, developing and running applications in the compute continuum requires mastering and managing the complexity of both the infrastructure (compute resources) and the available application components, in terms of distribution, heterogeneity, dynamicity and scale. To this end, the proposed research will aim at integrating intelligence in the continuum, by both facilitating and relying on AI techniques (a continuum for AI, AI for the continuum).
Principales activités
In the context identified above, the postdoc researcher is expected to contribute to MiMove’s research in one of the following sub-topics:
Automated development of IoT applications leveraging domain knowledge. Developing IoT applications for the compute continuum by relying on latest programming and execution paradigms (e.g., serverless, microservices, FaaS) brings many benefits to developers, but still requires managing a potentially large number of elementary services/functions. An alternative way for application design relies on methods from the goal-oriented requirement engineering domain, where high-level specifications of applications are elaborated in terms of goals. We leverage application domain knowledge in the form of knowledge graphs and NLP methods to provide automated recommendations to the developer for goal-driven application development.
Scheduling and running IoT systems in the compute continuum. Deploying IoT applications on the resource architecture while managing the constraints, requirements and tradeoffs of resources and QoS is a key problem related to the edge-fog-cloud continuum. Extending our previous results on the placement of data streaming operators on homogeneous, fixed-location resources, we aim to account for heterogeneous resources as well as possibly volatile ones coming from mobile and resource-constrained devices. This calls for dynamic lightweight scheduling algorithms that can tackle the runtime resource variability besides the dynamic application workloads. We aim to explore autonomous predictive scheduling approaches by relying on Reinforcement Learning and following the paradigm of Intent-based networking.
Ensuring interoperability in the compute continuum. To leverage its full potential, IoT applications deployed in the edge-fog-cloud continuum will rely on the composition of resources and system services across multiple providers, which is hampered by heterogeneity (e.g., diverse APIs, protocols, data formats, topologies). This makes interoperability in the continuum an essential problem for which effective solutions are required. Building on our previous results on IoT protocol interoperability, we aim at introducing runtime interoperability and portability solutions for automated composition of resources and system services in the dynamic compute continuum. To this end, we will explore solutions based on domain knowledge and NLP.
Automated Machine Learning in the compute continuum. Automated Machine Learning (AutoML) leverages algorithms and computational capabilities to automate key aspects of the machine learning pipeline, such as feature engineering, model selection, and hyperparameter tuning. We are particularly interested in developing AutoML solutions on IoT data streams that take into account the particularities and needs of the compute continuum.
ML inference in the compute continuum. ML inference systems for industrial plants have stringent performance requirements, while thay may have to cope with potentially limited computational/network resources (such as in rural settings). Additionally, they may host multiple ML inference processes with very different characteristics and priorities (such as ensuring human safety vs. predictive equipment maintenance). We aim at developing ML inference system solutions that are tailored to the compute continuum.
Compétences
The candidate should have a PhD in Computer Science with expertise – including experience in the implementation of related software prototypes – in one and possibly several of the following topics:
(Mobile) distributed systems, Middleware architectures and protocols, Software engineering, Internet of Things, Machine learning, Knowledge representation, Cloud/edge computing and scheduling.
Avantages
Subsidized meals Partial reimbursement of public transport costs Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.) Possibility of teleworking Professional equipment available (videoconferencing, loan of computer equipment, etc.) Social, cultural and sports events and activities Access to vocational training
Rémunération
According to civil service salary scales
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